The present invention relates to the field of data processing. More specifically, the present invention relates to a method and an apparatus for processing data in the medical field.
With the widespread move to medical electronization, Electronic Health Records (EHR) are used to record the relevant medical data of patients. Usually, an EHR includes basic information, main diseases, and all the clinical records of a patient. The clinical record records clinical conditions of a patient at different times (i.e., each time a patient visits a doctor, every day during a given time period, etc.), which includes diagnosis, examination, and lab test results (such as measurement results concerning various physiological states). The existing EHR typically uses the XML language to record patient data in the form of a Clinical Document Architecture (CDA).
On the other hand, authorities have issued clinical guidelines directed to different diseases with a lot of clinical practice and clinical evidence to help a doctor to fully understand the conditions of a patient. Generally, a clinical guideline includes a plurality of indications as well as judgment conditions of the indications. For instance, a clinical guideline concerning diabetes can contain an indication 1 of a controlled blood glucose and an indication 2 of a persistently high blood glucose. The condition of the indication 1 is that 80% of blood glucose value in the latest one month satisfies fasting blood glucose<7.5 mmol/L or 2 h blood glucose<10 mmol/L. The condition of the indication 2 is that 80% of blood glucose value in latest three months satisfies fasting blood glucose>=9 mmol/L or 2 h blood glucose>=13 mmol/L. Determination of a single indication cannot be directly used for performing diagnosis and treatment of a disease, but a combination of a plurality of indications can help a doctor to fully acquire comprehensive information of a patient. Since judgment and matching of indications can be based on measurement results of a patient, therefore, it is desired to acquire patient data from an EHR and process patient data to match it with indication conditions in a clinical guideline and to provide more comprehensive patient information.
FIG. 1 schematically shows the manner of processing patient data in the prior art. As shown in FIG. 1, when matching and analysis are required for a plurality of indication conditions 1-n in a clinical guideline, a data acquisition step 101, a data conversion step 102, and a condition matching step 103 are executed one by one as to each indication condition. Specifically, as to a certain indication condition i, at the data acquisition step 101, patient data required by the indication condition i is acquired. For instance, as to indication condition of the above indication 1, it is required to obtain fasting blood glucose data and 2 h blood glucose data of a patient in the latest one month. Then, at the data conversion step 102, the obtained data is converted into a certain needed form. As mentioned above, the existing EHR uses a XML language to record the patient data in form of CDA. However, this form is not convenient for direct data analysis and matching. Therefore, at step 102, patient data is converted from the CDA form to a form of Virtual Medical Record (VMR). The VMR form can be represented as a tree structure with the patient as a root node and respective attributes of observed results as leaf nodes. By going through the tree structure, at conditional matching step 103, the patient data can be matched with the indication condition i, which means to analyze whether the patient data meets the indication condition i. After matching the indication condition i, the next indication condition is analyzed according to the same steps 101-103. Thus, matching condition of the patient data with the respective indication conditions in the clinical guideline can be obtained by processing and analyzing the patient data.
However, the above manner of processing the patient data is not ideal in execution efficiency. The non-ideal efficiency is partly caused by redundant data processing. For instance, in order to analyze the indication condition 1, it is required to obtain fasting blood glucose data and 2 h blood glucose data of a patient in the latest one month. In order to analyze the indication condition 2, fasting blood glucose data and 2 h blood glucose data of the patient in the latest three months is obtained. Though data required by the indication condition 2 covers the data required by the indication condition 1, according to the method of FIG. 1, when the indication condition 2 is analyzed, it still requires retrieval again from an EHR of all the blood glucose data in the latest three months. As a result, the blood glucose data in the recent one month is retrieved repeatedly when analyzing the indication condition 1 and the indication condition 2. Further, at the data conversion step 102, the data above is converted again. At condition matching step 103, the above data is traversed for many times again. Obviously, such redundant processing reduces processing efficiency of the patient data. In practice, a clinical guideline for a certain disease usually contains more than one hundred, or even hundreds, of indication conditions. Since there are many indication conditions to be analyzed, processing of the patient data usually costs a lot of time and cannot be performed in real-time. This makes a doctor unable to obtain comprehensive information of a patient in efficient time.
Therefore, an improved solution is desired to improve processing efficiency of patient data.